This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron

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The Household Power Consumption dataset is a multivariate time series dataset which describes the electricity consumption over four years for a single household. They were tested to predict for a specific house and block of houses over a given period of time. Throughout the past couple of decades energy demand has increased exponentially. This increase loads the electricity distributors heavily. So forecasting future demand for electricity use would give the dealer an upper hand. Predicting the consumption of energy requires several parameters. This paper proposes two methods with one using a Recurrent Neural Network (RNN) and another using a Long Short Term Memory (LSTM) network, considering only the previous consumption of electricity to estimate potential consumption of electricity. To assess the applicability of the RNN and the LSTM network to predict the electricity consumption

Multivariate Time Series Forecasting with LSTMs in Keras

Time Series Forecasting LSTM based on Pytorch developed with Streamlit. Supports Univariate, Multivariate and Multi-Step Forecasting.

Multivariate Time-Series Forecasting with LSTM and Attention Mechanism - SJSU (2021): Used Python and TensorFlow package to predict the remaining useful life of Turbofan engine applying LSTM and Attention Mechanism in an Encoder-Decoder architecture.

Multivariate time series forecasting cryptocurrencies price with LSTMs in Keras

Demand prediction for Uber based on Multivariate Time Series Forecasting with LSTM (long short-term memory)

Multivariate Time Series Forecasting with LSTM in Keras adapted to my problem

Multivariate Time Series Forecasting with LSTM

Multivariate Time Series Forecasting with LSTMs in Keras

Multivariate Time Series Forecasting with LSTM in TensorFlow 2.x

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Multivariate Time Series Forecasting with LSTMs in Keras

Multivariate Time Series Forecasting with LSTM (Using Keras)

For predicting the temperature at any time step, used multiple approaches such as Univariate and Multivariate time series forecasting with Single and Multi-step using Long Short Term Memory(LSTM) Networks

This project focuses on advanced time series forecasting using deep learning models such as LSTM with Attention and Transformers. It includes synthetic multivariate data generation, preprocessing, feature engineering, SARIMAX baseline comparison, model training, and evaluation using MAE, RMSE, and MAPE

Multivariate Time Series Forecasting with LSTM

Multivariate Time Series Forecasting with LSTMs in Keras

Multivariate Time Series Forecasting with LSTMs in Keras

Forecasting Unemployment with Multivariate Time Series using a combination of VAR and LSTM

Significant Wave Height Forecasting using comparative machine learning approaches (Linear Regression, Random Forest, XGBoost, and LSTM) on multivariate buoy data with proper time-series validation and seasonal analysis.

This project implements advanced multivariate time series forecasting using deep learning with an attention mechanism. It covers data preprocessing, sequence generation, LSTM-based modeling with attention, training, evaluation, and prediction, demonstrating improved accuracy over basic models.

Advanced multivariate time-series forecasting project using deep learning with attention mechanisms. Includes data preprocessing, baseline LSTM model, Transformer-based attention model, hyperparameter tuning, comparative evaluation using RMSE/MAE, and attention-weight interpretation for feature importance

“Advanced multivariate time-series forecasting project using deep learning and attention mechanisms. Implements a full Transformer encoder–decoder, LSTM and ARIMA baselines, rigorous preprocessing, hyperparameter tuning, and detailed evaluation using RMSE, MAE, and MAPE with attention-based insights.”

Multivariate time series forecasting that aims to predict the next hour's weather forecast based on the previous 24 hour window. Final deep learning project for CSC 578 (Neural Networks and Deep Learning). Base model consisted of a recurrent neural network (RNN) with a single LSTM layer, followed by hyperparameter tuning and variations in depth intended to achieve a closer fit to the data.

Multivariate Time Series Forecasting with LSTMs in Keras

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Wind Speed Prediction Multivariate Time Series Forecasting with LSTMs